ludzkie-ciekawostki: Regression toward the mean

Evidence: 100 • authority: 72

In statistics, regression toward the mean (also called regression to the mean, reversion to the mean, and reversion to mediocrity) is the phenomenon where if one sample of a random variable is extreme, the next sampling of the same random variable is likely to be closer to its mean. Furthermore, when many random variables are sampled and the most extreme results are intentionally picked out, it refers to the fact that (in many cases) a second sampling of these picked-out variables will result in "less extreme" results, closer to the initial mean of all of the variables. Mathematically, the strength of this "regression" effect is dependent on whether or not all of the random variables are dr…

Evidence: 52 • authority: 88

Evidence: 52 • authority: 92

Evidence: 45 • authority: 88

Evidence: 45 • authority: 92

Beyond the Regression Effect: Investigating Time-Order Effects on the Cross-Modal Correspondence of Brightness and Loudness Within the Global Psychophysical Theory. This exploratory experimental study investigates the contribution of stimuluspresentation order to the regression effect in cross-modal matching of brightness andloudness. Previous research has largely relied on the magnitude production paradigm toobtain individual cross-modal matches, which induces distinct stimulus roles (i. e., afixed standard vs. an adjustable comparison). The cross-modal matches typicallyexhibit a role-dependent regression effect, that is, they gravitate toward the mean of theadjusted stimulus’ range. This…

Evidence: 45 • authority: 88

Evidence: 45 • authority: 92

Evidence: 45 • authority: 72

Evidence: 45 • authority: 72

In statistics, linear regression is a model that estimates the relationship between a scalar response (dependent variable) and one or more explanatory variables (regressor or independent variable) related via a linear combination. A linear model with exactly one explanatory variable is a simple linear regression; a model with two or more explanatory variables is a multiple linear regression. In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those…

Evidence: 38 • authority: 72

In regression analysis, least squares is a method to determine the best-fit model by minimizing the sum of the squared residuals—the differences between observed values and the values predicted by the model. Least squares problems fall into two categories: linear or ordinary least squares and nonlinear least squares, depending on whether or not the model functions are linear in all unknowns. The linear least-squares problem occurs in statistical regression analysis; it has a closed-form solution. The nonlinear problem is usually solved by iterative refinement; at each iteration the system is approximated by a linear one, and thus the core calculation is similar in both cases. Polynomial lea…

Evidence: 38 • authority: 92

Factors Associated with Healthcare Professionals' Attitudes Toward Migrant Patients: A Cross-Sectional, Exploratory Study.. Objective : To determine healthcare professionals' attitudes toward migrant patients and explore the sociodemographic and migration-related factors associated with these attitudes. Methods : This descriptive, cross-sectional, exploratory study was conducted with 390 healthcare professionals reached through a Facebook-based professional community and subsequently via other social media platforms using snowball sampling. Data were collected using a Demographic Information Form and the Healthcare Professionals' Attitudes Toward Migrants Scale. Data Analysis involved the u…

Evidence: 38 • authority: 92

Regression to the mean in measures of attitudes related to stuttering, disability, metacognitive awareness, and emotional intelligence.. Introduction: A series of recent studies using the Public Opinion Survey of Human Attributes-Stuttering ( POSHA-S ) have documented an extreme case of regression to the mean (RTM) or a "crossover" effect in public attitudes toward stuttering. Respondents were previously sorted according to changes in their attitudes. The "crossover" effect revealed that respondents with the highest pre-test scores had the lowest post-test scores, those with the lowest pre-test scores had the highest post-test scores, and those with intermediate scores changed minimally. Me…